The present disclosure relates in general to natural language processing (NLP) systems, specifically NLP systems that include natural language generation (NLG), which include natural language translation (NLT) systems, natural language processing question & answer (NLP Q&A) systems, natural language dialogue systems and the like. More specifically, the present disclosure relates to a NLP system designed to integrate disfluencies with natural language (NL) outputs, wherein the disfluencies are selected and applied in a manner that communicates in natural language a level of confidence in the NL outputs.
NLP is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and humans using languages (i.e., natural languages). As such, NLP is related to the area of human-computer interaction. Among the challenges in implementing NLP systems is enabling computers to derive meaning from NL inputs, as well as the effective and efficient generation of NL outputs. Included among NLP systems are machine translation (MT) systems and NLP Q&A systems.
MT systems are computer-based tools that support the translation of speech and/or text from one human language to another. MT systems come in a variety of forms, including, for example, fully automated machine translation (FAMT) systems, human-assisted machine translation (HAMT) systems, machine-aided translation (MAT) systems, and the like.
NLT systems are a known type of MT system. NLT systems are computer-based tools that allow two or more individuals in more or less immediate interaction, typically through email or otherwise online, to communicate in different languages. For example, cross-linguistic communication systems allow two or more people who are not fluent in the same language to communicate with one another. Speech recognition and language translation technologies have improved sufficiently that cross linguistic communication can now be automatically supported by technology. As used in the present disclosure, references to a speaker and/or a hearer using a NLT system include scenarios in which the “speaker” produces communications by typing or writing, along with situations in which the “hearer” receives communications by reading text.
NLP Q&A systems answer natural language questions by querying data repositories and applying elements of language processing, information retrieval and machine learning to arrive at a conclusion. Such systems are able to assist humans with certain types of semantic query and search operations
It would be beneficial to effectively and efficiently control the manner in which human users experience errors in the NL outputs of NLP systems.